面向嵌入式平台的单目ORB-SLAM稠密化建图实现

doi:10.3778/j.issn.1002-8331.2104-0139

摘要/Abstract

摘要： 针对现有方法在机器人室内定位中无法同时满足高精度定位、快速处理及稠密地图重建的问题，在拥有跟踪、地图构建和回环检测三线程的ORB-SLAM3系统基础上设计了三维稠密地图构建算法，分别在跟踪阶段、局部光束法平差阶段（bundle adjustment，BA）和全局BA阶段，对满足需求的关键帧进行二次采样和位姿更新，然后通过关键帧和对应位姿计算得到三维点云，最终获得稠密地图。实验结果表明，所提方法在Jetson AGX Xavier嵌入式平台上对TUM数据集的定位速度达到了10.8?frame/s，均方根误差仅有0.213%，验证了该系统的高精度与快速性，可以满足机器人室内定位与建图需求。

关键词: 稠密化, 室内定位, 同时定位与地图构建（SLAM）, 深度相机, 关键帧, 三维重建

Abstract: The existing methods can not satisfy the requirements of high-precision, fast-processing localization and mapping in the indoor robot localization. ORB-SLAM3（oriented fast and rotated BRIEF-simultaneous localization and mapping 3） system, which has three parallel threads about tracking, mapping and loop closing, is the basis of the 3D dense mapping algorithm. The key frames meeting the requirements are resampled and the pose is updated in the tracking phase, local BA（bundle adjustment） and full BA. 3D point clouds are generated by the key frames and corresponding poses, thereby the dense map is obtained. The experiment results show that the positioning speed and the root-mean-square error reach 10.8 frame/s and 0.213% respectively, when the established system is operating on the Jetson AGX Xavier embedded platform with TUM data sets. The high precision and rapidity of the established system are verified, and can meet the requirements of indoor robot localization and mapping.

Key words: densification, indoor localization, simultaneous localization and mapping（SLAM）, depth camera, key frame, 3D reconstruction

马靖煊, 王红雨, 曹彦, 乔文超, 韩佼志, 吴昌学. 面向嵌入式平台的单目ORB-SLAM稠密化建图实现[J]. 计算机工程与应用, 2022, 58(16): 213-218.

MA Jingxuan, WANG Hongyu, CAO Yan, QIAO Wenchao, HAN Jiaozhi, WU Changxue. Implementation of Densified Mapping of Monocular ORB-SLAM for Embedded Platform[J]. Computer Engineering and Applications, 2022, 58(16): 213-218.

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